1. Field of the Invention
The present invention generally relates to deriving information regarding articles from indicia thereon or characteristics thereof such as may be used for sortation and other selective processes and, more particularly, to deriving information from a plurality of potentially inconsistent indicia or characteristics particularly for sorting articles for transport to various locations based on machine readable indicia.
2. Description of the Prior Art
Many commercial applications require different actions to be taken or processes to be performed on different articles which may be sequentially processed. In many such cases, the selection of the process or action is based upon characteristics of the articles themselves such as properties that may be determined through testing or inspection, as in the manufacture of electronic devices that may be differentiated by electrical properties or tolerances or, more often, by identification of some particular physical characteristic of each article, for example, sorting of mail for transportation to one of a large number of potential destinations based on the address information affixed thereto.
Sorting of mail is a particularly high-volume process which, as a practical matter, must be highly automated. In order to automate such a high-volume process, it is necessary to also automate the reading or distinguishing of addresses. Accordingly, optical character recognition (OCR) processes have been developed and have reached a relatively high level of sophistication in order to accommodate hand-written indicia as well as many different fonts of many different or even varying sizes. However, optical character recognition remains subject to errors and the data processing overhead is relatively large and the execution thereof correspondingly slow.
Therefore, it is common at the present time to provide additional markings such as bar codes or other machine readable indicia on articles to allow more rapid and more accurate reading to be performed. The reading of such indicia, of any type that particularly facilitates reading by machine (whether or not to the exclusion of being readily human-readable, as in the case of bar codes) will be collectively referred to hereinafter by the currently preferred apparatus for performing the function, called a bar code reader (BCR).
However, the amount of information which can be encoded in a bar code or similar machine-readable indicia may often be severely limited. For example, The familiar xe2x80x9cZip-codexe2x80x9d is often used as a five-digit number; three digits of which are sufficient to specify a routing through a particular processing and distribution center (PandDC) while five digits identifies a particular post office facility (from a plurality thereof serviced by a single PandDC). In recent years, nine digit xe2x80x9cZip-codesxe2x80x9d have been adopted and are increasingly used which are sufficient to specify a particular carrier route for delivery. However, it is desirable to provide an eleven bit code to allow sorting to delivery order of specific delivery locations within a given carrier route. Therefore, xe2x80x9cZip-codesxe2x80x9d will most often appear in a five or nine digit form while bar codes may be applied in a three, five, nine or eleven digit form and it is most likely that even if the nine digit form of xe2x80x9cZip-codexe2x80x9d is used and read to generate a bar code, a portion of the remainder of the address must be read to supply an additional two digits in order to support the desired sorting processes.
Further, such bar code or machine-readable indicia are subject to numerous sources of error such as being performed in response to a possibly erroneous optical character recognition process or incorrect bar codes being applied by a sender which do not correspond to the written address. Moreover, bar code or machine readable indicia may not be applied to all articles subjected to the automated process and the OCR processing cannot be omitted completely. At best, BCR processes can be used to supplement OCR processes and possibly reduce the average amount of time for data acquisition to control a selective process. BCR processes clearly cannot perform or be regarded as an alternative for OCR processes at the present time.
Unfortunately, while some gains in processing time may be achieved by using OCR and BCR processes together, both OCR and BCR processes represent largely independent but sometimes related sources of error. The basic effect of these increased number of sources of error is an increased number of articles that are rejected from the selective (e.g. sorting) process which is greater than the sum of the numbers of articles which would be independently rejected from the OCR and BCR processes, respectively. For example, rejection rates currently experienced for mixed mail (where bar codes may not appear on a significant percentage of articles) are about 40% representing rejection rate components of about 30% from OCR processes performed at the required rates on objects moving at required speeds and at realistic variation in location of written or printed addresses because the OCR results are in error (e.g. do not correspond to a real address) or the address cannot be read under the necessary conditions, about 2% from BCR processes where possible (since bar codes may not appear on all articles) and about 8% due to disagreement between OCR and BCR results.
The rejection rate greater than the sum of the OCR and BCR rejection rates can be understood from the fact that inconsistency between the OCR and BCR results also cause rejection of the article. At the present state of the art, the number of articles rejected under a combination of OCR and BCR processes in the exemplary mail sortation environment alluded to above is a severe limitation to the volume of articles that can be processed by a given sorting apparatus within an acceptable amount of time since the rejected articles must be manually sorted within the same overall time frame, particularly when the automated process can only be effectively applied to less than two-thirds of the articles. Even if the rejection rates were significantly lower, human intervention is typically required for each rejected article and the number of articles which can be manually processed by one person in a given amount of time is generally far lower than the article rejection rate currently being experienced using a combination of OCR and BCR processes. It is a significant indicator of the cost and time consumed by manual sorting and the value of decreasing rejection rates that very expensive, large and complex sorting machines are well-justified and cost effective even though capable of automating only slightly more than half of the manual sorting process.
It is therefore an object of the present invention to provide a technique of combining OCR and BCR processes in a way that reduces rejection rates to much lower levels than previously possible.
It is another object of the invention to provide a selective control or sortation apparatus having reduced rejection rate and correspondingly increased throughput.
In order to accomplish these and other objects of the invention, a method of combining results of a plurality of feature discriminating techniques applied to an article and a sequential selective processing apparatus employing the same are provided wherein the method includes steps of validating either a result of a first feature discrimination technique or a result of a second discrimination technique when the results of the respective feature discrimination techniques correspond to each other, validating or rejecting a result of the first feature discrimination technique against expected or permitted values, validating or rejecting a result of the second feature discrimination technique against expected or permitted values, and outputting a validated result for control of a sequential selective process while rejecting only articles where neither of the results of the first or second feature description techniques is validated.